Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations297827
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.3 MiB
Average record size in memory184.0 B

Variable types

Categorical10
Numeric13

Alerts

LNG is highly overall correlated with ZIPHigh correlation
ZIP is highly overall correlated with LNGHigh correlation
baths is highly overall correlated with beds and 1 other fieldsHigh correlation
beds is highly overall correlated with baths and 1 other fieldsHigh correlation
beds_was_null is highly overall correlated with propertyType and 1 other fieldsHigh correlation
propertyType is highly overall correlated with beds_was_nullHigh correlation
sqft is highly overall correlated with baths and 2 other fieldsHigh correlation
status is highly overall correlated with beds_was_nullHigh correlation
target is highly overall correlated with sqftHigh correlation
status is highly imbalanced (55.0%) Imbalance
cooling is highly imbalanced (55.5%) Imbalance
lotsize is highly skewed (γ1 = 27.78887577) Skewed
mean_school_distance is highly skewed (γ1 = 145.291614) Skewed

Reproduction

Analysis started2024-11-26 10:06:54.379570
Analysis finished2024-11-26 10:07:43.464085
Duration49.08 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

status
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
for sale
197214 
active
75247 
other
 
5428
new construction
 
5108
foreclosed
 
4706
Other values (4)
 
10124

Length

Max length22
Median length8
Mean length7.7555829
Min length5

Characters and Unicode

Total characters2309822
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowactive
2nd rowfor sale
3rd rowfor sale
4th rowfor sale
5th rowactive

Common Values

ValueCountFrequency (%)
for sale 197214
66.2%
active 75247
 
25.3%
other 5428
 
1.8%
new construction 5108
 
1.7%
foreclosed 4706
 
1.6%
pending 4081
 
1.4%
under contract showing 2459
 
0.8%
pre-foreclosed 2421
 
0.8%
auction 1163
 
0.4%

Length

2024-11-26T13:07:43.757206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:44.069247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
for 197214
39.0%
sale 197214
39.0%
active 75247
 
14.9%
other 5428
 
1.1%
new 5108
 
1.0%
construction 5108
 
1.0%
foreclosed 4706
 
0.9%
pending 4081
 
0.8%
under 2459
 
0.5%
contract 2459
 
0.5%
Other values (3) 6043
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 306212
13.3%
a 276083
12.0%
o 233193
10.1%
r 222216
9.6%
s 211908
9.2%
207240
9.0%
f 204341
8.8%
l 204341
8.8%
c 98671
 
4.3%
t 96972
 
4.2%
Other values (10) 248645
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2100161
90.9%
Space Separator 207240
 
9.0%
Dash Punctuation 2421
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 306212
14.6%
a 276083
13.1%
o 233193
11.1%
r 222216
10.6%
s 211908
10.1%
f 204341
9.7%
l 204341
9.7%
c 98671
 
4.7%
t 96972
 
4.6%
i 88058
 
4.2%
Other values (8) 158166
7.5%
Space Separator
ValueCountFrequency (%)
207240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2100161
90.9%
Common 209661
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 306212
14.6%
a 276083
13.1%
o 233193
11.1%
r 222216
10.6%
s 211908
10.1%
f 204341
9.7%
l 204341
9.7%
c 98671
 
4.7%
t 96972
 
4.6%
i 88058
 
4.2%
Other values (8) 158166
7.5%
Common
ValueCountFrequency (%)
207240
98.8%
- 2421
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2309822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 306212
13.3%
a 276083
12.0%
o 233193
10.1%
r 222216
9.6%
s 211908
9.2%
207240
9.0%
f 204341
8.8%
l 204341
8.8%
c 98671
 
4.3%
t 96972
 
4.2%
Other values (10) 248645
10.8%

propertyType
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
single-family
193294 
condo
46901 
townhouse
 
17156
other
 
15749
multi-family
 
9940
Other values (4)
 
14787

Length

Max length13
Median length13
Mean length10.9121
Min length4

Characters and Unicode

Total characters3249918
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle-family
2nd rowsingle-family
3rd rowsingle-family
4th rowtownhouse
5th rowother

Common Values

ValueCountFrequency (%)
single-family 193294
64.9%
condo 46901
 
15.7%
townhouse 17156
 
5.8%
other 15749
 
5.3%
multi-family 9940
 
3.3%
traditional 6945
 
2.3%
contemporary 3031
 
1.0%
mobile home 2593
 
0.9%
coop 2218
 
0.7%

Length

2024-11-26T13:07:44.300862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:44.475583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
single-family 193294
64.3%
condo 46901
 
15.6%
townhouse 17156
 
5.7%
other 15749
 
5.2%
multi-family 9940
 
3.3%
traditional 6945
 
2.3%
contemporary 3031
 
1.0%
mobile 2593
 
0.9%
home 2593
 
0.9%
coop 2218
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 422951
13.0%
l 416006
12.8%
n 267327
8.2%
e 234416
 
7.2%
m 221391
 
6.8%
a 220155
 
6.8%
s 210450
 
6.5%
y 206265
 
6.3%
- 203234
 
6.3%
f 203234
 
6.3%
Other values (12) 644489
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3044091
93.7%
Dash Punctuation 203234
 
6.3%
Space Separator 2593
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 422951
13.9%
l 416006
13.7%
n 267327
8.8%
e 234416
7.7%
m 221391
7.3%
a 220155
7.2%
s 210450
6.9%
y 206265
6.8%
f 203234
6.7%
g 193294
6.3%
Other values (10) 448602
14.7%
Dash Punctuation
ValueCountFrequency (%)
- 203234
100.0%
Space Separator
ValueCountFrequency (%)
2593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3044091
93.7%
Common 205827
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 422951
13.9%
l 416006
13.7%
n 267327
8.8%
e 234416
7.7%
m 221391
7.3%
a 220155
7.2%
s 210450
6.9%
y 206265
6.8%
f 203234
6.7%
g 193294
6.3%
Other values (10) 448602
14.7%
Common
ValueCountFrequency (%)
- 203234
98.7%
2593
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3249918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 422951
13.0%
l 416006
12.8%
n 267327
8.2%
e 234416
 
7.2%
m 221391
 
6.8%
a 220155
 
6.8%
s 210450
 
6.5%
y 206265
 
6.3%
- 203234
 
6.3%
f 203234
 
6.3%
Other values (12) 644489
19.8%

baths
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5917655
Minimum0
Maximum40
Zeros60
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:44.664354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2.5
Q33
95-th percentile4
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0093185
Coefficient of variation (CV)0.38943281
Kurtosis24.042004
Mean2.5917655
Median Absolute Deviation (MAD)0.5
Skewness2.4445332
Sum771897.75
Variance1.0187239
MonotonicityNot monotonic
2024-11-26T13:07:44.844557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2 119780
40.2%
3 66793
22.4%
2.5 37648
 
12.6%
4 24098
 
8.1%
1 15654
 
5.3%
5 7850
 
2.6%
2.25 6167
 
2.1%
3.5 5769
 
1.9%
1.5 3851
 
1.3%
6 2876
 
1.0%
Other values (38) 7341
 
2.5%
ValueCountFrequency (%)
0 60
 
< 0.1%
0.75 227
 
0.1%
1 15654
 
5.3%
1.25 1010
 
0.3%
1.5 3851
 
1.3%
1.75 1570
 
0.5%
2 119780
40.2%
2.25 6167
 
2.1%
2.5 37648
 
12.6%
2.75 850
 
0.3%
ValueCountFrequency (%)
40 1
 
< 0.1%
30 2
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 6
< 0.1%
19 1
 
< 0.1%
18 3
< 0.1%

fireplace
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
207601 
1
90226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297827
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

Length

2024-11-26T13:07:45.014282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:45.129462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

Most occurring characters

ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297827
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

Most occurring scripts

ValueCountFrequency (%)
Common 297827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 207601
69.7%
1 90226
30.3%

sqft
Real number (ℝ)

High correlation 

Distinct7510
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2056.4744
Minimum121
Maximum7940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:45.277197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile805
Q11300
median1820
Q32541
95-th percentile4130
Maximum7940
Range7819
Interquartile range (IQR)1241

Descriptive statistics

Standard deviation1067.0138
Coefficient of variation (CV)0.51885589
Kurtosis3.0661156
Mean2056.4744
Median Absolute Deviation (MAD)591
Skewness1.4682729
Sum6.124736 × 108
Variance1138518.6
MonotonicityNot monotonic
2024-11-26T13:07:45.445585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 1307
 
0.4%
1000 964
 
0.3%
1500 952
 
0.3%
1800 949
 
0.3%
1400 876
 
0.3%
1100 872
 
0.3%
2000 831
 
0.3%
1600 799
 
0.3%
1300 699
 
0.2%
800 690
 
0.2%
Other values (7500) 288888
97.0%
ValueCountFrequency (%)
121 1
 
< 0.1%
122 1
 
< 0.1%
130 2
< 0.1%
144 3
< 0.1%
146 1
 
< 0.1%
147 2
< 0.1%
150 2
< 0.1%
151 1
 
< 0.1%
160 1
 
< 0.1%
169 1
 
< 0.1%
ValueCountFrequency (%)
7940 1
 
< 0.1%
7935 3
< 0.1%
7934 3
< 0.1%
7932 1
 
< 0.1%
7930 2
< 0.1%
7929 1
 
< 0.1%
7926 3
< 0.1%
7922 1
 
< 0.1%
7920 2
< 0.1%
7908 1
 
< 0.1%

beds
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2343172
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:45.610096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum46
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0919372
Coefficient of variation (CV)0.3376098
Kurtosis37.921444
Mean3.2343172
Median Absolute Deviation (MAD)1
Skewness2.6444153
Sum963267
Variance1.1923269
MonotonicityNot monotonic
2024-11-26T13:07:45.763645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 145927
49.0%
4 64118
21.5%
2 54039
 
18.1%
5 20409
 
6.9%
1 5680
 
1.9%
6 4752
 
1.6%
7 1161
 
0.4%
8 859
 
0.3%
9 335
 
0.1%
10 199
 
0.1%
Other values (18) 348
 
0.1%
ValueCountFrequency (%)
1 5680
 
1.9%
2 54039
 
18.1%
3 145927
49.0%
4 64118
21.5%
5 20409
 
6.9%
6 4752
 
1.6%
7 1161
 
0.4%
8 859
 
0.3%
9 335
 
0.1%
10 199
 
0.1%
ValueCountFrequency (%)
46 1
 
< 0.1%
44 1
 
< 0.1%
42 1
 
< 0.1%
32 1
 
< 0.1%
28 2
 
< 0.1%
24 6
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 6
< 0.1%

stories
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8783757
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:45.952756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum91
Range90
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.063295
Coefficient of variation (CV)1.0984464
Kurtosis237.83771
Mean1.8783757
Median Absolute Deviation (MAD)0
Skewness11.88608
Sum559431
Variance4.2571864
MonotonicityNot monotonic
2024-11-26T13:07:46.141170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 159827
53.7%
2 90337
30.3%
4 23330
 
7.8%
3 15599
 
5.2%
9 3107
 
1.0%
6 1744
 
0.6%
1.5 1073
 
0.4%
5 716
 
0.2%
7 317
 
0.1%
8 298
 
0.1%
Other values (62) 1479
 
0.5%
ValueCountFrequency (%)
1 159827
53.7%
1.2 1
 
< 0.1%
1.3 2
 
< 0.1%
1.5 1073
 
0.4%
1.7 15
 
< 0.1%
1.75 22
 
< 0.1%
2 90337
30.3%
2.2 1
 
< 0.1%
2.5 165
 
0.1%
3 15599
 
5.2%
ValueCountFrequency (%)
91 1
 
< 0.1%
75 2
 
< 0.1%
70 2
 
< 0.1%
66 3
 
< 0.1%
64 2
 
< 0.1%
63 3
 
< 0.1%
62 1
 
< 0.1%
60 9
< 0.1%
58 1
 
< 0.1%
57 8
< 0.1%

target
Real number (ℝ)

High correlation 

Distinct29549
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466498.16
Minimum48550
Maximum2565000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:46.365798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48550
5-th percentile104000
Q1219000
median335000
Q3559000
95-th percentile1350000
Maximum2565000
Range2516450
Interquartile range (IQR)340000

Descriptive statistics

Standard deviation406995.47
Coefficient of variation (CV)0.87244819
Kurtosis5.9089666
Mean466498.16
Median Absolute Deviation (MAD)145100
Skewness2.2659354
Sum1.3893575 × 1011
Variance1.6564532 × 1011
MonotonicityNot monotonic
2024-11-26T13:07:46.528700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225000 1489
 
0.5%
249900 1398
 
0.5%
275000 1397
 
0.5%
299900 1395
 
0.5%
399000 1354
 
0.5%
325000 1353
 
0.5%
350000 1349
 
0.5%
199900 1295
 
0.4%
250000 1287
 
0.4%
375000 1260
 
0.4%
Other values (29539) 284250
95.4%
ValueCountFrequency (%)
48550 1
< 0.1%
48594 1
< 0.1%
48600 1
< 0.1%
48630 1
< 0.1%
48640 1
< 0.1%
48684 1
< 0.1%
48700 1
< 0.1%
48702 2
< 0.1%
48710 1
< 0.1%
48732 1
< 0.1%
ValueCountFrequency (%)
2565000 1
 
< 0.1%
2560000 1
 
< 0.1%
2559990 1
 
< 0.1%
2559000 2
 
< 0.1%
2558000 1
 
< 0.1%
2555000 1
 
< 0.1%
2550000 53
< 0.1%
2549990 2
 
< 0.1%
2549000 6
 
< 0.1%
2548800 1
 
< 0.1%

ZIP
Real number (ℝ)

High correlation 

Distinct4057
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53196.481
Minimum1103
Maximum99338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:46.694359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1103
5-th percentile13210
Q133055
median37411
Q378204
95-th percentile95829
Maximum99338
Range98235
Interquartile range (IQR)45149

Descriptive statistics

Standard deviation26812.491
Coefficient of variation (CV)0.50402752
Kurtosis-1.420567
Mean53196.481
Median Absolute Deviation (MAD)18267
Skewness0.22557708
Sum1.5843348 × 1010
Variance7.1890966 × 108
MonotonicityNot monotonic
2024-11-26T13:07:46.861248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33131 1443
 
0.5%
78245 1351
 
0.5%
34747 1293
 
0.4%
78253 1229
 
0.4%
78254 1206
 
0.4%
33132 1148
 
0.4%
33137 1118
 
0.4%
32137 1115
 
0.4%
33130 1089
 
0.4%
34746 1057
 
0.4%
Other values (4047) 285778
96.0%
ValueCountFrequency (%)
1103 1
 
< 0.1%
1104 10
< 0.1%
1105 7
< 0.1%
1107 2
 
< 0.1%
1108 14
< 0.1%
1109 17
< 0.1%
1118 7
< 0.1%
1119 6
 
< 0.1%
1128 4
 
< 0.1%
1129 6
 
< 0.1%
ValueCountFrequency (%)
99338 83
< 0.1%
99337 128
< 0.1%
99336 110
< 0.1%
99224 74
< 0.1%
99223 80
< 0.1%
99218 27
 
< 0.1%
99217 67
< 0.1%
99216 22
 
< 0.1%
99212 45
 
< 0.1%
99208 160
0.1%

LAT
Real number (ℝ)

Distinct4034
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.409488
Minimum25.557912
Maximum48.798606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:47.011322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25.557912
5-th percentile25.819714
Q128.905193
median32.753141
Q337.922656
95-th percentile43.499764
Maximum48.798606
Range23.240694
Interquartile range (IQR)9.0174625

Descriptive statistics

Standard deviation5.8807394
Coefficient of variation (CV)0.17602004
Kurtosis-0.56435425
Mean33.409488
Median Absolute Deviation (MAD)4.234411
Skewness0.58818134
Sum9950247.5
Variance34.583096
MonotonicityNot monotonic
2024-11-26T13:07:47.161485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.766206 1443
 
0.5%
29.401093 1351
 
0.5%
28.311495 1293
 
0.4%
29.469011 1229
 
0.4%
29.539126 1206
 
0.4%
36.751519 1166
 
0.4%
25.77789 1148
 
0.4%
25.816281 1118
 
0.4%
29.581507 1115
 
0.4%
25.768524 1089
 
0.4%
Other values (4024) 285669
95.9%
ValueCountFrequency (%)
25.557912 157
 
0.1%
25.560027 60
 
< 0.1%
25.572213 183
 
0.1%
25.595896 187
 
0.1%
25.596129 304
0.1%
25.606126 492
0.2%
25.63884 64
 
< 0.1%
25.652131 261
0.1%
25.654426 399
0.1%
25.659873 355
0.1%
ValueCountFrequency (%)
48.798606 96
< 0.1%
48.75094 44
 
< 0.1%
48.696127 87
< 0.1%
48.216792 3
 
< 0.1%
48.089968 63
< 0.1%
48.056723 137
< 0.1%
48.006311 64
< 0.1%
47.955367 34
 
< 0.1%
47.948393 8
 
< 0.1%
47.945519 103
< 0.1%

LNG
Real number (ℝ)

High correlation 

Distinct4034
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.203222
Minimum-123.42537
Maximum-69.573012
Zeros0
Zeros (%)0.0%
Negative297827
Negative (%)100.0%
Memory size2.3 MiB
2024-11-26T13:07:47.311361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-123.42537
5-th percentile-122.00638
Q1-97.714483
median-84.53483
Q3-80.490508
95-th percentile-75.013276
Maximum-69.573012
Range53.852357
Interquartile range (IQR)17.223975

Descriptive statistics

Standard deviation14.124172
Coefficient of variation (CV)-0.15486483
Kurtosis-0.22729647
Mean-91.203222
Median Absolute Deviation (MAD)6.606678
Skewness-0.95244097
Sum-27162782
Variance199.49223
MonotonicityNot monotonic
2024-11-26T13:07:47.479962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.182897 1443
 
0.5%
-98.730806 1351
 
0.5%
-81.595565 1293
 
0.4%
-98.797801 1229
 
0.4%
-98.725885 1206
 
0.4%
-119.680602 1166
 
0.4%
-80.176165 1148
 
0.4%
-80.171528 1118
 
0.4%
-81.218196 1115
 
0.4%
-80.203359 1089
 
0.4%
Other values (4024) 285669
95.9%
ValueCountFrequency (%)
-123.425369 11
 
< 0.1%
-123.230905 16
 
< 0.1%
-123.192759 10
 
< 0.1%
-123.133235 4
 
< 0.1%
-123.111691 17
< 0.1%
-123.094751 21
< 0.1%
-123.080181 6
 
< 0.1%
-123.064528 24
< 0.1%
-123.063616 41
< 0.1%
-123.058241 2
 
< 0.1%
ValueCountFrequency (%)
-69.573012 1
 
< 0.1%
-69.661276 34
< 0.1%
-69.843372 22
< 0.1%
-69.887309 44
< 0.1%
-70.231801 2
 
< 0.1%
-70.298093 13
 
< 0.1%
-70.31253 6
 
< 0.1%
-70.346877 6
 
< 0.1%
-70.36307 2
 
< 0.1%
-70.392013 6
 
< 0.1%

private_pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
258982 
1
38845 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297827
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

Length

2024-11-26T13:07:47.630106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:47.743009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297827
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 297827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258982
87.0%
1 38845
 
13.0%

year_built
Real number (ℝ)

Distinct214
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1981.381
Minimum1735
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:47.878027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1735
5-th percentile1920
Q11960
median1988
Q32007
95-th percentile2019
Maximum2022
Range287
Interquartile range (IQR)47

Descriptive statistics

Standard deviation32.277816
Coefficient of variation (CV)0.016290565
Kurtosis0.082953585
Mean1981.381
Median Absolute Deviation (MAD)23
Skewness-0.81296297
Sum5.9010876 × 108
Variance1041.8574
MonotonicityNot monotonic
2024-11-26T13:07:48.045689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 29215
 
9.8%
2006 8240
 
2.8%
2005 8023
 
2.7%
2007 7461
 
2.5%
2018 6733
 
2.3%
2004 5779
 
1.9%
2017 5379
 
1.8%
2016 5215
 
1.8%
2008 5069
 
1.7%
2003 4872
 
1.6%
Other values (204) 211841
71.1%
ValueCountFrequency (%)
1735 1
< 0.1%
1740 1
< 0.1%
1750 1
< 0.1%
1780 1
< 0.1%
1788 1
< 0.1%
1790 2
< 0.1%
1794 2
< 0.1%
1795 2
< 0.1%
1796 2
< 0.1%
1799 1
< 0.1%
ValueCountFrequency (%)
2022 3
 
< 0.1%
2021 44
 
< 0.1%
2020 2068
 
0.7%
2019 29215
9.8%
2018 6733
 
2.3%
2017 5379
 
1.8%
2016 5215
 
1.8%
2015 4099
 
1.4%
2014 3310
 
1.1%
2013 2576
 
0.9%

heating
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
forced air
126945 
no data
66684 
central
31805 
other
31331 
electric
 
10950
Other values (5)
30112 

Length

Max length14
Median length10
Mean length8.0778976
Min length3

Characters and Unicode

Total characters2405816
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcentral
2nd rowno data
3rd rowforced air
4th rowforced air
5th rowelectric

Common Values

ValueCountFrequency (%)
forced air 126945
42.6%
no data 66684
22.4%
central 31805
 
10.7%
other 31331
 
10.5%
electric 10950
 
3.7%
gas 10615
 
3.6%
heat pump 9197
 
3.1%
baseboard 4196
 
1.4%
wall 3515
 
1.2%
heating system 2589
 
0.9%

Length

2024-11-26T13:07:48.217844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:48.381381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
forced 126945
25.2%
air 126945
25.2%
no 66684
13.3%
data 66684
13.3%
central 31805
 
6.3%
other 31331
 
6.2%
electric 10950
 
2.2%
gas 10615
 
2.1%
heat 9197
 
1.8%
pump 9197
 
1.8%
Other values (4) 12889
 
2.6%

Most occurring characters

ValueCountFrequency (%)
r 332172
13.8%
a 326426
13.6%
e 230552
9.6%
o 229156
9.5%
205415
8.5%
d 197825
8.2%
c 180650
7.5%
t 155145
6.4%
i 140484
5.8%
f 126945
 
5.3%
Other values (11) 281046
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2200401
91.5%
Space Separator 205415
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 332172
15.1%
a 326426
14.8%
e 230552
10.5%
o 229156
10.4%
d 197825
9.0%
c 180650
8.2%
t 155145
7.1%
i 140484
6.4%
f 126945
 
5.8%
n 101078
 
4.6%
Other values (10) 179968
8.2%
Space Separator
ValueCountFrequency (%)
205415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2200401
91.5%
Common 205415
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 332172
15.1%
a 326426
14.8%
e 230552
10.5%
o 229156
10.4%
d 197825
9.0%
c 180650
8.2%
t 155145
7.1%
i 140484
6.4%
f 126945
 
5.8%
n 101078
 
4.6%
Other values (10) 179968
8.2%
Common
ValueCountFrequency (%)
205415
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2405816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 332172
13.8%
a 326426
13.6%
e 230552
9.6%
o 229156
9.5%
205415
8.5%
d 197825
8.2%
c 180650
7.5%
t 155145
6.4%
i 140484
5.8%
f 126945
 
5.3%
Other values (11) 281046
11.7%

cooling
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
central
188209 
no data
81196 
cooling system
 
11124
other
 
7046
wall
 
4312
Other values (5)
 
5940

Length

Max length14
Median length7
Mean length7.2504273
Min length4

Characters and Unicode

Total characters2159373
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno data
2nd rowno data
3rd rowcentral
4th rowcentral
5th rowcentral

Common Values

ValueCountFrequency (%)
central 188209
63.2%
no data 81196
27.3%
cooling system 11124
 
3.7%
other 7046
 
2.4%
wall 4312
 
1.4%
refrigeration 2125
 
0.7%
electric 1422
 
0.5%
ceiling fan 1103
 
0.4%
evaporative 958
 
0.3%
has heating 332
 
0.1%

Length

2024-11-26T13:07:48.561581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:48.711836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central 188209
48.1%
no 81196
20.7%
data 81196
20.7%
cooling 11124
 
2.8%
system 11124
 
2.8%
other 7046
 
1.8%
wall 4312
 
1.1%
refrigeration 2125
 
0.5%
electric 1422
 
0.4%
ceiling 1103
 
0.3%
Other values (4) 2725
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 360721
16.7%
t 292412
13.5%
n 285192
13.2%
e 216824
10.0%
l 210482
9.7%
r 204010
9.4%
c 203280
9.4%
o 113573
 
5.3%
93755
 
4.3%
d 81196
 
3.8%
Other values (10) 97928
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2065618
95.7%
Space Separator 93755
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 360721
17.5%
t 292412
14.2%
n 285192
13.8%
e 216824
10.5%
l 210482
10.2%
r 204010
9.9%
c 203280
9.8%
o 113573
 
5.5%
d 81196
 
3.9%
s 22580
 
1.1%
Other values (9) 75348
 
3.6%
Space Separator
ValueCountFrequency (%)
93755
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2065618
95.7%
Common 93755
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 360721
17.5%
t 292412
14.2%
n 285192
13.8%
e 216824
10.5%
l 210482
10.2%
r 204010
9.9%
c 203280
9.8%
o 113573
 
5.5%
d 81196
 
3.9%
s 22580
 
1.1%
Other values (9) 75348
 
3.6%
Common
ValueCountFrequency (%)
93755
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2159373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 360721
16.7%
t 292412
13.5%
n 285192
13.2%
e 216824
10.0%
l 210482
9.7%
r 204010
9.4%
c 203280
9.4%
o 113573
 
5.3%
93755
 
4.3%
d 81196
 
3.8%
Other values (10) 97928
 
4.5%

parking
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
no data
126895 
attached garage
70403 
2 spaces
29769 
1 space
15975 
detached garage
 
12582
Other values (10)
42203 

Length

Max length15
Median length7
Mean length9.4056818
Min length4

Characters and Unicode

Total characters2801266
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno data
2nd rowno data
3rd rowdetached garage
4th rowno data
5th rowno data

Common Values

ValueCountFrequency (%)
no data 126895
42.6%
attached garage 70403
23.6%
2 spaces 29769
 
10.0%
1 space 15975
 
5.4%
detached garage 12582
 
4.2%
carport 10620
 
3.6%
off street 6913
 
2.3%
other 4781
 
1.6%
3 spaces 4709
 
1.6%
on street 3724
 
1.3%
Other values (5) 11456
 
3.8%

Length

2024-11-26T13:07:48.878490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 126895
22.1%
data 126895
22.1%
garage 84901
14.8%
attached 70403
12.3%
spaces 39627
 
6.9%
2 29769
 
5.2%
1 15975
 
2.8%
space 15975
 
2.8%
detached 12582
 
2.2%
street 10637
 
1.9%
Other values (9) 40287
 
7.0%

Most occurring characters

ValueCountFrequency (%)
a 645668
23.0%
t 316958
11.3%
276119
9.9%
e 264050
9.4%
d 222462
 
7.9%
g 172268
 
6.1%
o 154858
 
5.5%
c 149207
 
5.3%
n 136935
 
4.9%
r 124025
 
4.4%
Other values (12) 338716
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2467509
88.1%
Space Separator 276119
 
9.9%
Decimal Number 55602
 
2.0%
Math Symbol 2036
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 645668
26.2%
t 316958
12.8%
e 264050
10.7%
d 222462
 
9.0%
g 172268
 
7.0%
o 154858
 
6.3%
c 149207
 
6.0%
n 136935
 
5.5%
r 124025
 
5.0%
s 105866
 
4.3%
Other values (5) 175212
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 29769
53.5%
1 15975
28.7%
3 4709
 
8.5%
4 3113
 
5.6%
5 2036
 
3.7%
Space Separator
ValueCountFrequency (%)
276119
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2467509
88.1%
Common 333757
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 645668
26.2%
t 316958
12.8%
e 264050
10.7%
d 222462
 
9.0%
g 172268
 
7.0%
o 154858
 
6.3%
c 149207
 
6.0%
n 136935
 
5.5%
r 124025
 
5.0%
s 105866
 
4.3%
Other values (5) 175212
 
7.1%
Common
ValueCountFrequency (%)
276119
82.7%
2 29769
 
8.9%
1 15975
 
4.8%
3 4709
 
1.4%
4 3113
 
0.9%
5 2036
 
0.6%
+ 2036
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2801266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 645668
23.0%
t 316958
11.3%
276119
9.9%
e 264050
9.4%
d 222462
 
7.9%
g 172268
 
6.1%
o 154858
 
5.5%
c 149207
 
5.3%
n 136935
 
4.9%
r 124025
 
4.4%
Other values (12) 338716
12.1%

lotsize
Real number (ℝ)

Skewed 

Distinct15406
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21561.59
Minimum0
Maximum9234720
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:49.028283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1724
Q14235
median7500
Q310125
95-th percentile43560
Maximum9234720
Range9234720
Interquartile range (IQR)5890

Descriptive statistics

Standard deviation151781.39
Coefficient of variation (CV)7.039434
Kurtosis1026.7858
Mean21561.59
Median Absolute Deviation (MAD)3265
Skewness27.788876
Sum6.4216237 × 109
Variance2.3037591 × 1010
MonotonicityNot monotonic
2024-11-26T13:07:49.213722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4235 33208
 
11.2%
8276 26425
 
8.9%
8141 5366
 
1.8%
1860 4672
 
1.6%
11325.6 2763
 
0.9%
10890 2680
 
0.9%
13503.6 2680
 
0.9%
6098 2401
 
0.8%
7405 2396
 
0.8%
12196.8 2358
 
0.8%
Other values (15396) 212878
71.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 131
< 0.1%
3 1
 
< 0.1%
4 5
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
13 3
 
< 0.1%
23 2
 
< 0.1%
ValueCountFrequency (%)
9234720 1
< 0.1%
9194645 1
< 0.1%
9147600 1
< 0.1%
8906278 1
< 0.1%
8906277.6 1
< 0.1%
8755560 1
< 0.1%
8738571.6 1
< 0.1%
8712000 1
< 0.1%
8058600 1
< 0.1%
7840800 1
< 0.1%

remodeling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
159323 
1
138504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297827
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

Length

2024-11-26T13:07:49.359705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:49.461486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

Most occurring characters

ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297827
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

Most occurring scripts

ValueCountFrequency (%)
Common 297827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159323
53.5%
1 138504
46.5%

lotsize_was_null
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
223187 
1
74640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297827
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

Length

2024-11-26T13:07:49.592882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:49.695810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

Most occurring characters

ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297827
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

Most occurring scripts

ValueCountFrequency (%)
Common 297827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223187
74.9%
1 74640
 
25.1%

mean_school_rating
Real number (ℝ)

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1716047
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:49.831000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q13.7
median5
Q36.5
95-th percentile8.5
Maximum10
Range9
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation1.9143043
Coefficient of variation (CV)0.37015674
Kurtosis-0.68174991
Mean5.1716047
Median Absolute Deviation (MAD)1.3
Skewness0.15731873
Sum1540243.5
Variance3.664561
MonotonicityNot monotonic
2024-11-26T13:07:49.994963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 18532
 
6.2%
4 16677
 
5.6%
5 15657
 
5.3%
3 14341
 
4.8%
6.3 12828
 
4.3%
3.3 12240
 
4.1%
7 11076
 
3.7%
5.3 10794
 
3.6%
3.7 10544
 
3.5%
4.7 10452
 
3.5%
Other values (77) 164686
55.3%
ValueCountFrequency (%)
1 1635
 
0.5%
1.2 11
 
< 0.1%
1.3 839
 
0.3%
1.4 33
 
< 0.1%
1.5 1449
 
0.5%
1.6 49
 
< 0.1%
1.7 1926
 
0.6%
1.8 212
 
0.1%
1.9 9
 
< 0.1%
2 7645
2.6%
ValueCountFrequency (%)
10 453
 
0.2%
9.8 135
 
< 0.1%
9.7 1160
 
0.4%
9.6 11
 
< 0.1%
9.5 703
 
0.2%
9.4 37
 
< 0.1%
9.3 2195
0.7%
9.2 472
 
0.2%
9 4710
1.6%
8.8 501
 
0.2%

mean_school_distance
Real number (ℝ)

Skewed 

Distinct271
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7502097
Minimum0
Maximum1590.8
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:50.165583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11
median1.7
Q33
95-th percentile9.4
Maximum1590.8
Range1590.8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.6035748
Coefficient of variation (CV)2.0375082
Kurtosis33045.471
Mean2.7502097
Median Absolute Deviation (MAD)0.8
Skewness145.29161
Sum819086.7
Variance31.400051
MonotonicityNot monotonic
2024-11-26T13:07:50.327448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14389
 
4.8%
1.2 13280
 
4.5%
0.8 12777
 
4.3%
1.1 12046
 
4.0%
0.9 12004
 
4.0%
1.4 11677
 
3.9%
1.3 10573
 
3.6%
1.6 10239
 
3.4%
0.7 9861
 
3.3%
0.6 9793
 
3.3%
Other values (261) 181188
60.8%
ValueCountFrequency (%)
0 45
 
< 0.1%
0.1 464
 
0.2%
0.2 2070
 
0.7%
0.3 3326
 
1.1%
0.4 6012
2.0%
0.5 6381
2.1%
0.6 9793
3.3%
0.7 9861
3.3%
0.8 12777
4.3%
0.9 12004
4.0%
ValueCountFrequency (%)
1590.8 1
 
< 0.1%
1187.5 1
 
< 0.1%
725.5 4
 
< 0.1%
725.4 1
 
< 0.1%
312.6 1
 
< 0.1%
122.5 1
 
< 0.1%
45.1 1
 
< 0.1%
40.9 1
 
< 0.1%
40.3 1
 
< 0.1%
39.7 182
0.1%

schools_count
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1735874
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-11-26T13:07:50.497023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile8
Maximum65
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.3661956
Coefficient of variation (CV)1.0461493
Kurtosis116.05514
Mean4.1735874
Median Absolute Deviation (MAD)0
Skewness9.444758
Sum1243007
Variance19.063664
MonotonicityNot monotonic
2024-11-26T13:07:50.633987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 172668
58.0%
8 32440
 
10.9%
2 30386
 
10.2%
5 22186
 
7.4%
4 17078
 
5.7%
6 7275
 
2.4%
1 6236
 
2.1%
7 3704
 
1.2%
24 1656
 
0.6%
10 633
 
0.2%
Other values (14) 3565
 
1.2%
ValueCountFrequency (%)
1 6236
 
2.1%
2 30386
 
10.2%
3 172668
58.0%
4 17078
 
5.7%
5 22186
 
7.4%
6 7275
 
2.4%
7 3704
 
1.2%
8 32440
 
10.9%
9 355
 
0.1%
10 633
 
0.2%
ValueCountFrequency (%)
65 333
 
0.1%
63 509
 
0.2%
61 99
 
< 0.1%
49 218
 
0.1%
24 1656
0.6%
22 393
 
0.1%
21 55
 
< 0.1%
17 1
 
< 0.1%
16 91
 
< 0.1%
15 90
 
< 0.1%

beds_was_null
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
238493 
1
59334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters297827
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Length

2024-11-26T13:07:50.782624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:07:50.882682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297827
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
Common 297827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238493
80.1%
1 59334
 
19.9%

Interactions

2024-11-26T13:07:39.486001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:10.233103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:12.545258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.284112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.446535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.477476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:22.160397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:25.679193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.089818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.459944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:32.607518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.728606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.898329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:39.743613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:10.441567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:12.716766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.454976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.613229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.644169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:22.407507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:25.857673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.270983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.629644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:32.771264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.896502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:37.525172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:39.954564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:10.653440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:12.895503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.606678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.790453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.804850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:22.664629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:26.146765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.445661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.812025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.032101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.078658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:37.700944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.110110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:10.815238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:13.066813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.762481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.931978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.966403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:22.910579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:26.407725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.588693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.970780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.202678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.219985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:37.856996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.265386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:10.975172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:13.234507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.922899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.103342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:20.120733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:23.177230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:26.587978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.730604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.118799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.345703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.373020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.015551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.424211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.136312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:13.412739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.095257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.273183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:20.324690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:23.418834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:26.784823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:28.893753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.300231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.494683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.532796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.178283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.581311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.292890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:13.587437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.250941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.423963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:20.722685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:23.654885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:26.963891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:29.067414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.463011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.653641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.688745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.335289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.745406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.441751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:13.750435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.390271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.569340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:20.991758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:24.027550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.136034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:29.229980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.617988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.799180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:35.847831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.500487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:40.902729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.594824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:14.413988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.549247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.723056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:21.178740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:24.355962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.304033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:29.409258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.803565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:33.953779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.039838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.670984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:41.066557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.743952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:14.577088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.707834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:18.870968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:21.330845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:24.690910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.462017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:29.725632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:31.956373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.103946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.279002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.829772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:41.213697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:11.893034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:14.748765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:16.850206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.012530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:21.478646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:24.959157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.611107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:29.882324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:32.110324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.247447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.421289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:38.984124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:41.382916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:12.039118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:14.910615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.029119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.161180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:21.623172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:25.212186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.756158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.044092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:32.276798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.398132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.567312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:39.133687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:41.555560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:12.261691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:15.113295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:17.188651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:19.323835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:21.952809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:25.443815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:27.915626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:30.266039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:32.449564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:34.566265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:36.743009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:07:39.315331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-26T13:07:50.999069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
LATLNGZIPbathsbedsbeds_was_nullcoolingfireplaceheatinglotsizelotsize_was_nullmean_school_distancemean_school_ratingparkingprivate_poolpropertyTyperemodelingschools_countsqftstatusstoriestargetyear_built
LAT1.000-0.2410.1660.0230.0730.1320.1780.3320.183-0.0350.195-0.378-0.1460.1360.1740.1550.332-0.0350.0310.1170.0640.080-0.189
LNG-0.2411.000-0.939-0.064-0.0880.1550.2800.2440.235-0.0890.1990.172-0.0470.1490.1350.1970.230-0.035-0.1320.1170.112-0.033-0.132
ZIP0.166-0.9391.0000.0420.0650.1520.1930.2900.1860.0540.118-0.1430.0650.1620.1520.1710.3180.0550.1070.105-0.0890.0380.111
baths0.023-0.0640.0421.0000.5470.1020.0210.1080.0290.0880.0770.0310.1720.0590.1270.0520.014-0.0600.6210.0280.1850.4040.222
beds0.073-0.0880.0650.5471.0000.0810.0270.0170.0230.2640.057-0.0110.0790.0390.0200.1380.0200.0110.6560.022-0.1030.2490.061
beds_was_null0.1320.1550.1520.1020.0811.0000.3370.0750.4980.0140.1270.0000.0610.4760.1310.6100.1090.1570.2040.6140.0340.0100.035
cooling0.1780.2800.1930.0210.0270.3371.0000.1640.3290.0080.2160.0000.0500.1670.1470.1390.2690.1130.0460.1670.0070.0320.081
fireplace0.3320.2440.2900.1080.0170.0750.1641.0000.1930.0190.2500.0060.1010.2300.0230.1880.0620.0530.2900.0770.0280.1280.088
heating0.1830.2350.1860.0290.0230.4980.3290.1931.0000.0120.2740.0030.0560.2260.2050.1720.3440.1530.0490.2700.0160.0370.086
lotsize-0.035-0.0890.0540.0880.2640.0140.0080.0190.0121.0000.0280.1470.1150.0110.0180.0350.0190.1180.3130.004-0.3100.0430.001
lotsize_was_null0.1950.1990.1180.0770.0570.1270.2160.2500.2740.0281.0000.0000.1050.2500.0290.4840.1610.0530.2540.1410.0560.0160.221
mean_school_distance-0.3780.172-0.1430.031-0.0110.0000.0000.0060.0030.1470.0001.0000.1730.0050.0040.0000.0030.1810.0660.000-0.037-0.0540.264
mean_school_rating-0.146-0.0470.0650.1720.0790.0610.0500.1010.0560.1150.1050.1731.0000.0680.1230.0700.0770.0390.2320.0490.0690.3410.224
parking0.1360.1490.1620.0590.0390.4760.1670.2300.2260.0110.2500.0050.0681.0000.1970.1580.2450.2010.1040.2360.0110.0590.113
private_pool0.1740.1350.1520.1270.0200.1310.1470.0230.2050.0180.0290.0040.1230.1971.0000.1760.1560.1630.1210.2580.0500.1360.150
propertyType0.1550.1970.1710.0520.1380.6100.1390.1880.1720.0350.4840.0000.0700.1580.1761.0000.1200.0710.1600.1860.0700.0630.126
remodeling0.3320.2300.3180.0140.0200.1090.2690.0620.3440.0190.1610.0030.0770.2450.1560.1201.0000.3120.0870.2420.0120.0410.284
schools_count-0.035-0.0350.055-0.0600.0110.1570.1130.0530.1530.1180.0530.1810.0390.2010.1630.0710.3121.0000.0370.256-0.108-0.0360.079
sqft0.031-0.1320.1070.6210.6560.2040.0460.2900.0490.3130.2540.0660.2320.1040.1210.1600.0870.0371.0000.0340.0410.5110.239
status0.1170.1170.1050.0280.0220.6140.1670.0770.2700.0040.1410.0000.0490.2360.2580.1860.2420.2560.0341.0000.0100.0390.055
stories0.0640.112-0.0890.185-0.1030.0340.0070.0280.016-0.3100.056-0.0370.0690.0110.0500.0700.012-0.1080.0410.0101.0000.1990.124
target0.080-0.0330.0380.4040.2490.0100.0320.1280.0370.0430.016-0.0540.3410.0590.1360.0630.041-0.0360.5110.0390.1991.0000.125
year_built-0.189-0.1320.1110.2220.0610.0350.0810.0880.0860.0010.2210.2640.2240.1130.1500.1260.2840.0790.2390.0550.1240.1251.000

Missing values

2024-11-26T13:07:41.826023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T13:07:42.544055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statuspropertyTypebathsfireplacesqftbedsstoriestargetZIPLATLNGprivate_poolyear_builtheatingcoolingparkinglotsizeremodelinglotsize_was_nullmean_school_ratingmean_school_distanceschools_countbeds_was_null
0activesingle-family3.512900.004.01.0418000.02838735.179251-79.37648902019centralno datano data8276.0015.25.58.00
1for salesingle-family3.001947.003.02.0310000.09921647.686363-117.21668102019no datano datano data5828.0004.01.33.00
2for salesingle-family8.016457.005.03.02395000.07520532.835893-96.79551402006forced aircentraldetached garage8220.0109.20.84.00
3for saletownhouse3.00897.002.02.0209000.01914539.909857-75.19826501920forced aircentralno data680.0001.40.42.00
4activeother2.001507.003.01.0181500.03475928.103908-81.41937802006electriccentralno data4996.0102.33.84.01
5activesingle-family2.501192.983.01.068000.03811535.053329-89.86280601976no datano datano data8750.0002.71.13.01
6activesingle-family2.003588.003.01.0244900.05040143.153169-93.19982001970forced aircentralno data124582.0003.86.46.00
7for salesingle-family3.001930.003.02.0311995.07708029.815894-95.52288502019gascentralattached garage2056.0003.01.13.00
8for salecoop2.001300.003.06.0669000.01135440.768208-73.82740301965no datano dataattached garage75358.8006.71.53.00
9activeother2.003130.003.02.0260000.07706830.007063-95.48836202015centralcentralno data5715.0104.22.95.01
statuspropertyTypebathsfireplacesqftbedsstoriestargetZIPLATLNGprivate_poolyear_builtheatingcoolingparkinglotsizeremodelinglotsize_was_nullmean_school_ratingmean_school_distanceschools_countbeds_was_null
297817for salesingle-family2.50950.02.01.0799500.07821229.464611-98.49365311938wallcentralno data3746.0104.01.33.00
297818for salesingle-family3.011792.04.02.0280000.07708029.815894-95.52288501970othercentraldetached garage6599.0102.70.73.00
297819for salesingle-family2.011829.03.01.0171306.03280528.529380-81.40366701962forced aircentral1 space7704.0102.31.33.00
297820activesingle-family2.501895.03.01.0199900.07611032.707831-97.33826501921no datacentralno data7500.0005.01.33.01
297821for salesingle-family2.001841.04.01.0252990.07708929.586959-95.22560102019no datano data2 spaces8276.0016.01.83.00
297822for salecondo3.001417.02.03.0799000.02000138.910353-77.01773902010forced aircentral1 space4235.0013.00.22.00
297823for salesingle-family6.004017.05.01.01249000.03318025.960389-80.14311311990othercentral2 spaces8500.0107.516.62.00
297824for salecondo3.002000.03.09.0674999.06065741.940293-87.64685701924otherno datanone4235.0014.34.13.00
297825for salesingle-family3.001152.03.02.0528000.01143440.676808-73.77642501950otherno data2 spaces1600.0104.50.62.00
297826for salesingle-family2.001462.03.01.0204900.07821829.490048-98.39713502019electriccentralno data6969.0004.01.83.00